Probabilistic Sequence Mining – Evaluation and Extension of ProMFS Algorithm for Real-Time Problems

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Sequential pattern mining is an extensively studied method for data mining. One of new and less documented approaches is estimation of statistical characteristics of sequence for creating model sequences, that can be used to speed up the process of sequence mining. This paper proposes extensive modifications to one of such algorithms, ProMFS (probabilistic algorithm for mining frequent sequences), which notably increases algorithm’s processing speed by a significant reduction of its computational complexity. A new version of algorithm is evaluated for real-life and artificial data sets and proven to be useful in real-time applications and problems.

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International Journal of Electronics and Telecommunications

The Journal of Committee of Electronics and Telecommunications of Polish Academy of Sciences

Journal Information

CiteScore 2016: 0.72

SCImago Journal Rank (SJR) 2016: 0.248
Source Normalized Impact per Paper (SNIP) 2016: 0.542


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